Characterizing Patients at Risk for Sepsis Through Big Data SUMMARY The goal of this KL2 research proposal is create an extension of an existing data-driven sepsis algorithm, the artificial intelligence sepsis expert (AISE), by forecasting the type and sequence of sepsis-specific organ failure using clinical data from the electronic medical record, then identifying the incremental benefit received by adding high-resolution data derived from cardiovascular waveforms (arterial waveform and electrocardiographic waves), and small molecule metabolite data over time to provide some mechanistic context. The most important data (features) used in real-time from AISE will be used as inputs for a fuzzy k- means clustering algorithm (?Saving Organs from Sepsis?, or SOS) using retrospectively-collected data, designed to better characterize patients at risk for sepsis by their organ failure. I have selected three organs in particular: shock, acute respiratory failure, and acute kidney injury (AKI). Principal component analyses (PCA) data from 2,375 ICU patients with sepsis will be projected onto a novel visual representation for patient phenotyping based on risk of (trajectory toward) different types of organ failure. I will identify if this SOS algorithm can accurately forecast new organ failure within 12 hours based on SOS. To better understand the impact of specific features on organ failure, I will test the ability of each high- resolution features, and metabolomics data to forecast septic shock. Among those who develop septic shock, I will measure all nine high-resolution features from the beginning of the ICU stay up to shock onset and compare those changes to those who develop sepsis but not septic shock, and those who do not develop sepsis. I will then see if the collective addition of high-resolution data improves performance of septic shock forecasting. Finally, I will conduct a prospective observational study to collect metabolomic information in a 60- patient study to identify the incremental improvement of adding metabolomics data to SOS for predicting septic shock, over SOS with just EMR and waveform data. The results of this work will provide preliminary data for further career development and NIH-funding. The long-term goal would be to build a model that optimizes the timing of appropriate therapy, thus decreasing the incidence of sepsis and associated organ failure. As a K23 candidate, I will use this award to acquire formal didactic training and more hands-on experience in machine learning, signal processing, metabolomics analysis. I will seek focused training that will complement my experience as a clinical trialist so that I can design high-quality studies to contribute to Big Data analytics in critical care research and practice. My overarching career goal is to become a leader in the application of Big Data analysis of critically ill patients to predict progression of disease, specifically sepsis. The Emory environment is an ideal place to develop these capabilities. Emory Healthcare houses over 200 medical, surgical, and subspecialty ICU beds, many of which are ?wired? to store streaming data. In addition to the physical resources, my development will be enhanced by my superb mentorship team, led by Dr. Greg Martin.